// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#ifndef EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H
#define EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H

namespace Eigen {

/** \class TensorCustomUnaryOp
  * \ingroup CXX11_Tensor_Module
  *
  * \brief Tensor custom class.
  *
  *
  */
namespace internal {
    template <typename CustomUnaryFunc, typename XprType> struct traits<TensorCustomUnaryOp<CustomUnaryFunc, XprType>>
    {
        typedef typename XprType::Scalar Scalar;
        typedef typename XprType::StorageKind StorageKind;
        typedef typename XprType::Index Index;
        typedef typename XprType::Nested Nested;
        typedef typename remove_reference<Nested>::type _Nested;
        static const int NumDimensions = traits<XprType>::NumDimensions;
        static const int Layout = traits<XprType>::Layout;
        typedef typename traits<XprType>::PointerType PointerType;
    };

    template <typename CustomUnaryFunc, typename XprType> struct eval<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Eigen::Dense>
    {
        typedef const TensorCustomUnaryOp<CustomUnaryFunc, XprType> EIGEN_DEVICE_REF type;
    };

    template <typename CustomUnaryFunc, typename XprType> struct nested<TensorCustomUnaryOp<CustomUnaryFunc, XprType>>
    {
        typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> type;
    };

}  // end namespace internal

template <typename CustomUnaryFunc, typename XprType>
class TensorCustomUnaryOp : public TensorBase<TensorCustomUnaryOp<CustomUnaryFunc, XprType>, ReadOnlyAccessors>
{
public:
    typedef typename internal::traits<TensorCustomUnaryOp>::Scalar Scalar;
    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
    typedef typename XprType::CoeffReturnType CoeffReturnType;
    typedef typename internal::nested<TensorCustomUnaryOp>::type Nested;
    typedef typename internal::traits<TensorCustomUnaryOp>::StorageKind StorageKind;
    typedef typename internal::traits<TensorCustomUnaryOp>::Index Index;

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCustomUnaryOp(const XprType& expr, const CustomUnaryFunc& func) : m_expr(expr), m_func(func) {}

    EIGEN_DEVICE_FUNC
    const CustomUnaryFunc& func() const { return m_func; }

    EIGEN_DEVICE_FUNC
    const typename internal::remove_all<typename XprType::Nested>::type& expression() const { return m_expr; }

protected:
    typename XprType::Nested m_expr;
    const CustomUnaryFunc m_func;
};

// Eval as rvalue
template <typename CustomUnaryFunc, typename XprType, typename Device> struct TensorEvaluator<const TensorCustomUnaryOp<CustomUnaryFunc, XprType>, Device>
{
    typedef TensorCustomUnaryOp<CustomUnaryFunc, XprType> ArgType;
    typedef typename internal::traits<ArgType>::Index Index;
    static const int NumDims = internal::traits<ArgType>::NumDimensions;
    typedef DSizes<Index, NumDims> Dimensions;
    typedef typename internal::remove_const<typename ArgType::Scalar>::type Scalar;
    typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    static const int PacketSize = PacketType<CoeffReturnType, Device>::size;
    typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
    typedef StorageMemory<CoeffReturnType, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;

    enum
    {
        IsAligned = false,
        PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
        BlockAccess = false,
        PreferBlockAccess = false,
        Layout = TensorEvaluator<XprType, Device>::Layout,
        CoordAccess = false,  // to be implemented
        RawAccess = false
    };

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockNotImplemented TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_STRONG_INLINE TensorEvaluator(const ArgType& op, const Device& device) : m_op(op), m_device(device), m_result(NULL)
    {
        m_dimensions = op.func().dimensions(op.expression());
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }

    EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data)
    {
        if (data)
        {
            evalTo(data);
            return false;
        }
        else
        {
            m_result = static_cast<EvaluatorPointerType>(m_device.get((CoeffReturnType*)m_device.allocate_temp(dimensions().TotalSize() * sizeof(Scalar))));
            evalTo(m_result);
            return true;
        }
    }

    EIGEN_STRONG_INLINE void cleanup()
    {
        if (m_result)
        {
            m_device.deallocate_temp(m_result);
            m_result = NULL;
        }
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_result[index]; }

    template <int LoadMode> EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
    {
        return internal::ploadt<PacketReturnType, LoadMode>(m_result + index);
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
    {
        // TODO(rmlarsen): Extend CustomOp API to return its cost estimate.
        return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
    }

    EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_result; }

#ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const { m_result.bind(cgh); }
#endif

protected:
    void evalTo(EvaluatorPointerType data)
    {
        TensorMap<Tensor<CoeffReturnType, NumDims, Layout, Index>> result(m_device.get(data), m_dimensions);
        m_op.func().eval(m_op.expression(), result, m_device);
    }

    Dimensions m_dimensions;
    const ArgType m_op;
    const Device EIGEN_DEVICE_REF m_device;
    EvaluatorPointerType m_result;
};

/** \class TensorCustomBinaryOp
  * \ingroup CXX11_Tensor_Module
  *
  * \brief Tensor custom class.
  *
  *
  */
namespace internal {
    template <typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> struct traits<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>>
    {
        typedef typename internal::promote_storage_type<typename LhsXprType::Scalar, typename RhsXprType::Scalar>::ret Scalar;
        typedef typename internal::promote_storage_type<typename LhsXprType::CoeffReturnType, typename RhsXprType::CoeffReturnType>::ret CoeffReturnType;
        typedef typename promote_storage_type<typename traits<LhsXprType>::StorageKind, typename traits<RhsXprType>::StorageKind>::ret StorageKind;
        typedef typename promote_index_type<typename traits<LhsXprType>::Index, typename traits<RhsXprType>::Index>::type Index;
        typedef typename LhsXprType::Nested LhsNested;
        typedef typename RhsXprType::Nested RhsNested;
        typedef typename remove_reference<LhsNested>::type _LhsNested;
        typedef typename remove_reference<RhsNested>::type _RhsNested;
        static const int NumDimensions = traits<LhsXprType>::NumDimensions;
        static const int Layout = traits<LhsXprType>::Layout;
        typedef typename conditional<Pointer_type_promotion<typename LhsXprType::Scalar, Scalar>::val,
                                     typename traits<LhsXprType>::PointerType,
                                     typename traits<RhsXprType>::PointerType>::type PointerType;
    };

    template <typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
    struct eval<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Eigen::Dense>
    {
        typedef const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>& type;
    };

    template <typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType> struct nested<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>>
    {
        typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> type;
    };

}  // end namespace internal

template <typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType>
class TensorCustomBinaryOp : public TensorBase<TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, ReadOnlyAccessors>
{
public:
    typedef typename internal::traits<TensorCustomBinaryOp>::Scalar Scalar;
    typedef typename Eigen::NumTraits<Scalar>::Real RealScalar;
    typedef typename internal::traits<TensorCustomBinaryOp>::CoeffReturnType CoeffReturnType;
    typedef typename internal::nested<TensorCustomBinaryOp>::type Nested;
    typedef typename internal::traits<TensorCustomBinaryOp>::StorageKind StorageKind;
    typedef typename internal::traits<TensorCustomBinaryOp>::Index Index;

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorCustomBinaryOp(const LhsXprType& lhs, const RhsXprType& rhs, const CustomBinaryFunc& func)

        : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_func(func)
    {
    }

    EIGEN_DEVICE_FUNC
    const CustomBinaryFunc& func() const { return m_func; }

    EIGEN_DEVICE_FUNC
    const typename internal::remove_all<typename LhsXprType::Nested>::type& lhsExpression() const { return m_lhs_xpr; }

    EIGEN_DEVICE_FUNC
    const typename internal::remove_all<typename RhsXprType::Nested>::type& rhsExpression() const { return m_rhs_xpr; }

protected:
    typename LhsXprType::Nested m_lhs_xpr;
    typename RhsXprType::Nested m_rhs_xpr;
    const CustomBinaryFunc m_func;
};

// Eval as rvalue
template <typename CustomBinaryFunc, typename LhsXprType, typename RhsXprType, typename Device>
struct TensorEvaluator<const TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType>, Device>
{
    typedef TensorCustomBinaryOp<CustomBinaryFunc, LhsXprType, RhsXprType> XprType;
    typedef typename internal::traits<XprType>::Index Index;
    static const int NumDims = internal::traits<XprType>::NumDimensions;
    typedef DSizes<Index, NumDims> Dimensions;
    typedef typename XprType::Scalar Scalar;
    typedef typename internal::remove_const<typename XprType::CoeffReturnType>::type CoeffReturnType;
    typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
    static const int PacketSize = PacketType<CoeffReturnType, Device>::size;

    typedef typename Eigen::internal::traits<XprType>::PointerType TensorPointerType;
    typedef StorageMemory<CoeffReturnType, Device> Storage;
    typedef typename Storage::Type EvaluatorPointerType;

    enum
    {
        IsAligned = false,
        PacketAccess = (PacketType<CoeffReturnType, Device>::size > 1),
        BlockAccess = false,
        PreferBlockAccess = false,
        Layout = TensorEvaluator<LhsXprType, Device>::Layout,
        CoordAccess = false,  // to be implemented
        RawAccess = false
    };

    //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//
    typedef internal::TensorBlockNotImplemented TensorBlock;
    //===--------------------------------------------------------------------===//

    EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) : m_op(op), m_device(device), m_result(NULL)
    {
        m_dimensions = op.func().dimensions(op.lhsExpression(), op.rhsExpression());
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }

    EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data)
    {
        if (data)
        {
            evalTo(data);
            return false;
        }
        else
        {
            m_result =
                static_cast<EvaluatorPointerType>(m_device.get((CoeffReturnType*)m_device.allocate_temp(dimensions().TotalSize() * sizeof(CoeffReturnType))));
            evalTo(m_result);
            return true;
        }
    }

    EIGEN_STRONG_INLINE void cleanup()
    {
        if (m_result != NULL)
        {
            m_device.deallocate_temp(m_result);
            m_result = NULL;
        }
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { return m_result[index]; }

    template <int LoadMode> EIGEN_DEVICE_FUNC PacketReturnType packet(Index index) const
    {
        return internal::ploadt<PacketReturnType, LoadMode>(m_result + index);
    }

    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const
    {
        // TODO(rmlarsen): Extend CustomOp API to return its cost estimate.
        return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, PacketSize);
    }

    EIGEN_DEVICE_FUNC EvaluatorPointerType data() const { return m_result; }

#ifdef EIGEN_USE_SYCL
    // binding placeholder accessors to a command group handler for SYCL
    EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void bind(cl::sycl::handler& cgh) const { m_result.bind(cgh); }
#endif

protected:
    void evalTo(EvaluatorPointerType data)
    {
        TensorMap<Tensor<CoeffReturnType, NumDims, Layout>> result(m_device.get(data), m_dimensions);
        m_op.func().eval(m_op.lhsExpression(), m_op.rhsExpression(), result, m_device);
    }

    Dimensions m_dimensions;
    const XprType m_op;
    const Device EIGEN_DEVICE_REF m_device;
    EvaluatorPointerType m_result;
};

}  // end namespace Eigen

#endif  // EIGEN_CXX11_TENSOR_TENSOR_CUSTOM_OP_H
